Lightweight deep learning models for histopathological image analysis in resource-constrained healthcare environments

Abstract

Histopathological images analysis is an essential task in diagnosing cancer, but the application of artificial intelligence (AI) in computational pathology is often constrained by the heavy computational demands of convolutional neural networks (CNNs), Vision Transformers (ViTs), and pathology foundation models due to their computational cost, memory requirements, inference time, and infrastructural needs. This review explores the role of lightweight deep learning models in histopathological image analysis in terms of their diagnostic accuracy, computational efficiency, explainability, and deployability in resource-constrained healthcare environments. Recent advancements in lightweight CNNs, compact ViTs, hybrid CNN-Transformer architectures, efficient attention mechanisms, and optimization algorithms were discussed. The paper also addresses the issue of benchmark histopathology datasets, evaluation procedures, and the necessity of reporting not only clinical performance but also computational metrics like parameters, floating-point operations per second (FLOPs), inference time, memory usage, and the platform for deploying the model. Issues such as insufficient amounts of annotated data, staining, and scanner variability, domain shift between institutions, poor external validation, inadequate explainability, and lack of benchmarking are among the key challenges mentioned. In general, the review indicates that there is an effective way towards scalable, interpretable, and clinically useful computational pathology, particularly in resource-constrained healthcare environments where accuracy must be balanced with efficiency and real-world deployability.